A 53-year-old patient who had experienced 3 hours of chest pain had a 12-lead electrocardiogram performed, and the results are as shown. He was given sublingual nitroglycerin and developed severe symptomatic hypotension. His blood pressure normalized with volume resuscitation.

The right-sided leads indicate ST-segment elevations in RV<inf>3</inf> to RV<inf>5</inf>, which are consistent with a right ventricular infarct.

Timing of release of various cardiac biomarker peaks after the onset of myocardial infarction

Modified 2-dimensional (top) echocardiogram and color flow Doppler image (bottom). Apical 4-chamber views show a breach in the interventricular septum and free communication between ventricles through a large apical septum ventricular septal defect in a patient who recently had an anterior myocardial infarction.

Apical 2-chamber view depicts a large left ventricular apical thrombus with mobile extensions.

Parasternal long-axis view of the left ventricle demonstrates a large inferobasal aneurysm. Note the wide neck and base of the aneurysm.

Acute myocardial infarct. At 3 days, there is a zone of yellow necrosis surrounded by darker hyperemic borders. The arrow points to a transmural infarct in the posterior wall of the left ventricle, in this short axis slice through the left and right ventricular chambers.

Acute myocardial infarction, reperfusion type. In this case, the infarct is diffusely hemorrhagic. There is a rupture track through the center of this posterior left ventricular transmural infarct. The mechanism of death was hemopericardium.

Healing myocardial infarction, lateral left ventricle. In this heart, there is a variegated or mottled appearance to the lateral left ventricle (left). This infarct began 19 days prior to death.

Early healed myocardial infarction, anterior septum. There is a glistening gelatinous appearance to this infarction, which occurred 6 weeks prior to death, from embolization during valve surgery.

Healed myocardial infarction, anterior left ventricle. There is diffuse scarring (white) with marked thinning of the ventricle (aneurysm).

Acute myocardial infarct. The earliest change is hypereosinophilia (above) with an intense pink cytoplasm. There is no inflammation at border between the necrotic myocardium and the viable myocardium (left and below), indicating that the necrosis is about 12-24 hours in age.

Acute myocardial infarct. After 24 hours, there is a neutrophilic infiltrate at the border of the infarct. Viable myocardium is at the left, and neutrophils with apoptosis (karyorrhexis) are seen infiltrating the necrotic muscle. This patient experienced abdominal pain 35 hours prior to death.

Healing myocardial infarct. This patient died 8 days after experiencing sudden chest pain at rest. There is a large area of necrosis with hypereosinophilia of myocytes, with a rim of viable myocardium at the very bottom. At the border, there is chronic inflammation with early granulation tissue, with ingrowth of endothelial cells.

Healing myocardial infarct. At 10 days to 2 weeks, there is chronic inflammation, hemosiderin-laden macrophages, and early fibroblasts without significant collagen deposition.

Healed myocardial infarct. At 3 months, there is dense scar, which is blue on this Masson trichrome stain. This infarct was subendocardial, in the posterior left ventricle near the ventricular septum.

This is a posteroanterior view of a right ventricular endocardial activation map during ventricular tachycardia in a patient with a previous septal myocardial infarction. Earliest activation is recorded in red; late activation shows as blue to magenta. Fragmented low-amplitude diastolic local electrocardiograms were recorded adjacent to the earliest (red) breakout area, and local ablation in this scarred zone (red dots) resulted in termination and noninducibility of this previously incessant arrhythmia.

A color-enhanced angiogram of the heart left shows a plaque-induced obstruction (top center) in a major artery, which can lead to myocardial infarction (MI). MIs can precipitate heart failure.

of
21

Tables

Table 1.Absolute and Relative Contraindications to Fibrinolytic Therapy in Patients with STEMI

Since the loadings of the PC are only partially conserved between any two studies (including the phases) and there is no biological reason for axes of variance to be orthogonal, we also adopted an analysis of nine conserved axes of variation following the strategy we recently defined from re-analysis of multiple peripheral blood gene expression profiling datasets [
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]. Each axis represents strong co-variance of several hundred transcripts with a correlation coefficient r > 0.7 that appear to represent different aspects of immune function. They are defined by an axis score that is generated as PC1 for a set of 10 blood informative transcripts that we have shown consistently correlate with the respective axis [
17
]. Each of these axis scores explains 70% to 95% of the variation in the set of 10 blood informative transcripts, compared with just approximately 25% for any randomly chosen set of 10 transcripts. Multiple regression of all nine axis scores on all of the transcripts was performed for each phase, and over 5,070 probes associated with at least one axis in either phase. Cross-matching of the list of significant associations between the two phases showed, on average, 81% overlap, ranging from 61% for axis 9 to 91% for axis 3. Those transcripts that are significantly associated at the approximate Bonferroni threshold of < 10 in both phases (2,432 probes) were retained as axis-associated transcripts (Additional file
5
).

Differential gene expression between classes of subject (AMI versus non-AMI; cardiovascular death versus remainder; drug treatments) was evaluated by analysis of variance on the normalized data. Volcano plots [] show the significance for each probe as the negative logarithm of the -value (NLP) against the magnitude of difference (log 2 scale, 1 represents a 2-fold change).

For survival analysis, both cohorts were pooled and PC1 scores were categorized by outcome specific receiver operating characteristic (ROC) analyses using Youden’s index (Sensitivity - (1 - Specificity)) [] to identify the threshold for 'high' and 'low' cardiovascular death-associated PC1 scores in both cohorts separately. The relationship between PC1 score and outcomes was determined using the Cox proportional-hazards regression in unadjusted models and in models adjusted for established risk factors that included age, gender, BMI, serum creatinine, diabetes, hypertension, hyperlipidemia, smoking, statin use, AMI and CAD (>50% luminal stenosis) all at baseline. The ability of the standard clinical model for predicting adverse events was calculated using the C-statistic before and after addition of the PC1 score.

Gene enrichment analysis was performed with the ToppGene Suite [], and showed a highly significant enrichment (hypergeometric = 4 × 10) for 18 genes annotated to the set of 222 genes known to be up-regulated in CD133 relative to CD133 hematopoietic stem cells in the 'Jaatinen_HSC_Dn' Molecular Signatures Database (MSigDB) [] entry. No other significant multiple comparison-adjusted enrichments were reported.

A perspective transformation leading to transformative learning, however, occurs much less frequently. Mezirow believes that this less frequent transformation usually results from a "disorienting dilemma", which is triggered by a life crisis or major life transition, although it may also result from an accumulation of transformations in meaning schemes over a period of time.
[11]

A number of critical responses to Mezirow's theory of transformative learning have emerged over the years.
[14]
One criticism of Mezirow's theory is its emphasis upon rationality. Some studies support Mezirow. Others conclude that Mezirow grants rational critical reflection too much importance.
[15]

Edward W. Taylor
[16]
has since suggested neurobiological research as a promising area that may offer some explanation about the role emotions play, closing the gap between rationality and emotion in the transformative learning process. Taylor implies that, with available modern technology such as
magnetic resonance imaging
(MRI) and
positron emission tomography
(PET), these once obscure factors can now be examined through determining which neurological brain systems are at work during disorienting dilemmas and the journey of recovery that follows. This neurobiological research also stresses the importance of the role of
implicit memory
, from which emerge habits, attitudes and preferences that are related to unconscious thoughts and actions.

While the learning process is certainly rational on some levels, it is also a profound experience that can be described as a spiritual or emotional transformation as well. The experience of undoing racist, sexist, and other oppressive attitudes can be painful and emotional, as these attitudes have often been developed as ways to cope with and make sense of the world. This type of learning requires taking risks, and a willingness to be vulnerable and have one's attitudes and assumptions challenged.

Other theorists have proposed a view of transformative learning as an intuitive and emotional process. John M. Dirkx, Robert D. Boyd, J. Gordon Myers, and Rosemary R. Ruether link Mezirow's rational, cognitive and analytical approach to a more intuitive, creative and holistic view of transformative learning.
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This view of transformative learning is based primarily on the work of Robert Boyd,
[18]
who has developed a theory of transformative education based on
analytical (or depth) psychology
.

find_peak

An overview of
incidence
is provided below in the worked example below. More detailed tutorials are distributed as vignettes with the package:

The following worked example provides a brief overview of the package's functionalities. See the
vignettes section
for more detailed tutorials.

This example uses the simulated Ebola Virus Disease (EVD) outbreak from the package
outbreaks
. We will compute incidence for various time steps, calibrate two exponential models around the peak of the epidemic, and analyse the results.

First, we load the data:

We compute the weekly incidence:

incidence
can also compute incidence by specified groups using the
groups
argument. For instance, we can compute the weekly incidence by gender:

incidence
objects can be manipulated easily. The
[
operator implements subetting of dates (first argument) and groups (second argument). For instance, to keep only the first 20 weeks of the epidemic:

Some temporal subsetting can be even simpler using
subset
, which permits to retain data within a specified time window:

Subsetting groups can also matter. For instance, let's try and visualise the incidence based on onset of symptoms by outcome:

where
y
is the incidence,
r
is the growth rate,
t
is the number of days since a specific point in time (typically the start of the outbreak), and
b
is the intercept.

Such model can be fitted to any incidence object using
fit
. Of course, a single log-linear model is not sufficient for modelling our time series, as there is clearly an growing and a decreasing phase. As a start, we can calibrate a model on the first 20 weeks of the epidemic: